Direct Expected Quadratic Utility Maximization for Mean-Variance Controlled Reinforcement Learning

Subjective mean-variance preferences without expected utility

Classical derivations of mean variance preferences have all relied upon the expected utility hypothesis. Some widespread experimental studies have uncovered that the expected utility model tends to be systematically violated in practice. Such findings would lead people to doubt the empirical relevance of the literature and the practical effectiveness of the portfolio selection which employ the ...

Expected Qualitative Utility Maximization

A model for decision making that generalizes Expected Utility Maximization is presented. This model, Expected Qualitative Utility Maximization, encompasses the Maximin criterion. It relaxes both the Independence and the Continuity postulates. Its main ingredient is the definition of a qualitative order on nonstandard models of the real numbers and the consideration of nonstandard utilities. Exp...

The Test of Mean-Variance Approximations to Non-Expected Utility

This article tests the performance of the mean-variance approximations to non-expected utility functions. Previous studies justify mean-variance analysis only in the context of expected utility theory by demonstrating the excellent empirical performance of the mean-variance approximations to two of the commonly used utility functions in the study of expected utility: the power and exponential u...

Mean-variance versus expected utility in dynamic investment analysis

Forward-backward systems for expected utility maximization

In this paper we deal with the utility maximization problem with a general utility function. We derive a new approach in which we reduce the utility maximization problem with general utility to the study of a fully-coupled Forward-Backward Stochastic Differential Equation (FBSDE). AMS Subject Classification: Primary 60H10, 93E20 JEL Classification: C61, D52, D53